Ontology Learning Algorithm

AKA: Ontology Learner, Ontology Learning from Text Algorithm.
Context:
- It can be applied by an ontology learning system.
- It can be:
  - a Supervised Ontology Learning Algorithm.
  - a Bootstrapped Ontology Learning Algorithm.
  - a Heuristic Ontology Learning Algorithm, e.g. based on lexico-syntactic patterns.
- …
Counter-Example(s):
- a Terminology Extraction Algorithm.
See: Text Mining Algorithm, Knowledge Discovery Algorithm.

References

(Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/ontology_learning Retrieved:2014-2-16.
- Ontology learning (ontology extraction, ontology generation, or ontology acquisition) is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between those concepts from a corpus of natural language text, and encoding them with an ontology language for easy retrieval. As building ontologies manually is extremely labor-intensive and time consuming, there is great motivation to automate the process.
  Typically, the process starts by extracting terms and concepts or noun phrases from plain text using linguistic processors such as part-of-speech tagging and phrase chunking. Then statistical ^[1] or symbolic ^[2] ^[3]
  techniques are used to extract relation signatures.

↑ A. Maedche and S. Staab. Learning ontologies for the semantic web. In Semantic Web Workshop 2001.
↑ Marti A. Hearst. Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the Fourteenth International Conference on Computational Linguistics, pages 539--545, Nantes, France, July 1992.
↑ Roberto Navigli and Paola Velardi. Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites, Computational Linguistics, 30(2), MIT Press, 2004, pp. 151-179.